Abstract

Computational prediction of a movie's financial success usually relies only on metadata such as - genre, budget, actors, Motion Picture Association of America (MPAA) rating and critics' reviews. We argue that movie trailers, created to invoke viewers' interest and curiosity about a movie, carry complementary information for predicting a movie's financial future. We created a database consisting of 474 American movie trailers along with various metadata and information about movie's financial success in the opening weekend. A number of features that capture the emotional information contained in the audiovisual stream of the trailers are designed and extracted. We observe that the content-based features have as much predictive information as the meta features. Through regression analysis on our database, we show that signal information from trailer content improves the prediction performance.

Date

2016

Authors

Adarsh Tadimari, Naveen Kumar, Tanaya Guha, Shrikanth S Narayanan

Conference

2016 IEEE international conference on acoustics, speech and signal processing (ICASSP)

Pages

2777-2781

Publisher

IEEE